A novel temporal recommendation method based on user query topic evolution

Abstract

Personalized product recommendation provides online customers with options in line with consumers’ interests and preferences to assist users in making purchasing decisions. Previous personalized recommendation methods identify user preferences by analyzing historical purchase data. As the user preferences reflected in the user–item matrix take only two values: like or dislike, the binary purchase data-based method is too constrained to capture individual diverse opinions and user preferences evolution. Despite the fact that online search queries explicitly express consumers’ content preferences for product attributes, very little research has focused on inferring user preferences from online queries. To this end, this study proposes a novel temporal recommendation method based on estimating consumers’ dynamic preferences from their search queries. Our approach involves: first, proposing a temporal topic model that utilizes a Markov process to predict users’ potential interests, and then integrating customer preferences and product attribute features into a unified product ranking model to obtain personalized and diversified product lists. Empirical analysis shows that the proposed approach can better capture the temporal dynamics of customer preferences drift and can more accurately predict consumer purchase intentions. Furthermore, products recommended by the proposed approach not only better match individual preferences, but also cover more diverse content topics.